System and method for assessing animals considering auscultation and evaluation of physiological responses in various environments

ABSTRACT

The invention includes a system and method for predicting the performance of production animals by analysis of heart and lung sounds to determine likelihoods the animals will develop BRD or other diseases or ailments. Vital signs of animals are recorded during an adrenergic sympathetic “flight or fight” situation. A cardio-pulmonary rate ratio is determined for each animal by dividing a normalized adjusted heart rate value by a normalized adjusted respiratory value. From the ratios calculated for each animal in a group, a ratio range is established. Ratio values at a lower end of the ratio range indicate higher relative respiration rates and poor lung performance due to disease. Ratio values at an upper end of the range may indicate low cardiac output and an inability to tolerate rapid weight gain. Ratio values at either end of the range may indicate compromised cardio-pulmonary function. Animals can be further classified by weight, and the ratio values within weight classes are used to generate probabilities for BRD or other diseases.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119(e) to U.S.Provisional Patent Application Ser. No. 61/985,935 filed Apr. 29, 2014,which is incorporated herein in its entirety by reference.

FIELD OF THE INVENTION

The present invention generally relates to non-invasive evaluations ofanimals for indications of tolerance to stress or history of disease ortrauma as measured by cardio-pulmonary function. More particularly, to asystem and method for predicting: the performance of production animals,the capacity of activities for all animals, and the likelihood ofmorbidity and mortality by analysis of thoracic heart and lung sounds.

BACKGROUND OF THE INVENTION

Cardiovascular diseases, respiratory diseases, and gastrointestinaldiseases have been distinguished according to sounds auscultated fromthe body of a patient. Based upon measurements taken of the differentsounds, medical practitioners have been able to diagnose diseases andproceed with treatments.

In order to make a precise diagnosis of an ailment based uponauscultated sounds, extensive empirical knowledge of various and diverseforms of auscultated sounds is necessary. Until recently, auscultationwas more art than science since making a diagnosis was based mainly uponthe trained ear of a caregiver and not based upon objectively measureddata from recorded sounds.

With the advent of digital/electronic stethoscopes, auscultated soundscan be recorded in digital form, and computer programs manipulate thedata to analyze characteristics of the recording. From this analysis,more precise diagnoses can be made based upon objective criteria and notjust upon the trained ear of the attending caregiver.

It is well known to measure auscultated sounds from humans in order tomake diagnoses of perceived pathology. However, auscultation for animalssuch as cattle is used infrequently. There have been very few effortsmade to gather data from auscultated sounds for purposes of makingconclusions as to the type of disease that may be occurring in a speciesof animal.

Particularly in a feed yard where it is necessary for cattle to bemaintained at an optimum state of health for maximum weight gain tooccur, it is critical that sick cattle be identified early for effectivetreatment and to contribute to biosecurity. The true state of health forcattle can be difficult to measure using traditional techniques such asobservation of symptoms to include temperature, posture and visual signs(e.g. nasal discharge, depression, and abdominal fill.). In the exampleof the bovine species, case definitions for BRD traditionally include aminimally effective but objective rectal temperature and a subjectiveclinical score. Clinical trials indicate that objective lung scoresprovide stronger correlations than rectal temperatures to ultimate casefatality rates, retreatment rates, and therefore treatment costs. Cattleare visually evaluated when they first arrive at the feed yard, and thesurge of adrenalin associated with handling, along with prey defensivemechanisms, can often mask disease symptoms. Stethoscopic evaluation ofbovine heart and lung sounds can be used to evaluate thecardio-pulmonary efficiency or potential efficiency of cattle duringvarious stages of arrival processing. However, because of the lack ofcurrent data in objectively categorizing animal heart and lung sounds,there is a need for developing an automated system and method that canassist a caregiver in assessing these sounds and making timelydiagnoses.

Bovine respiratory disease is complex and is particularly difficult toaccurately diagnose in the harsh environments where the animal's healthassessment takes place; noisy with uncooperative patients at bestrequiring server restraint. The thick musculature that surrounds thethorax of cattle, the heavy hide and layers of fat renders the use of astethoscope difficult to obtain sounds that can be interpreted forpurposes of making an accurate diagnosis. Because of the difficultiesencountered to effectively gather auscultated sounds from cattle, and ageneral lack of knowledge in the cattle industry as to how to interpretthese sounds, the cattle industry has been slow in developing automateddiagnostic processes that can effectively use data generated throughauscultation.

Production animals are intentionally metabolically stressed to promoterapid weight gain in the feed yard. Nutrition technology and healthmanagement protocols strive to maximize daily weight gain, but weightgain itself can push the physiological limits of production animalsbeyond their ability to mount a compensatory response to; metabolicchallenges, disease, weather environmental, and behavioral stresses.Determining the physiological capacity for stress of each productionanimal would allow for matching of the animal to an optimal productionmanagement strategy protocol. This optimization would enhance theproduction process causing a higher rate of return by minimizingvaluable asset loss due to animal variations in abilities to handlephysiological stress. If an animal's compensatory capabilities could bepredicted prior to exposing the animal to the stresses inherent inproduction, then optimal production procedures could be implemented foreach animal given its unique physiological profile and thus maximizingeach animal's potential and minimizing each animals risks.

Cardiac performance/efficiency is measured by the cardiac output (CO) ofan animal and is defined as heart rate (HR) multiplied by the strokevolume (SV) thus this relationship can be expressed as CO=(HR)(SV). Theheart rate of an animal will typically increase at times of acute stress(both physical and psychological) due to increased automaticity (andtherefore an increased rate) caused by catecholamine release during asympathetic adrenergic response to stress. For production animals suchas beef cattle, processing actions such as experienced in transportingthe animals to and sorting the animals within a feed yard can be aseries of very stressful events that predictably drive up the heartrates of the animals due to positive chronotropic effects of thesympathetic nervous system.

Respiratory performance/efficiency of an animal is the ability of thepulmonary system to adequately exchange gases allowing for metabolicvariations while maintaining optimal functioning of vital organs. Partof the mechanism for handling the variations of metabolic changes isthrough perfusion matching with ventilation. Ventilation or respiratorydrive, can in part be measured by respiratory rate.

When either the cardiac function or respiratory function is impaired,the other system may respond through compensatory mechanisms thatattempt to maintain homeostasis for the animal. Maintaining homeostasisbecomes more challenging if an animal is placed in stressfulenvironments. Maintaining homeostasis under an impaired condition willconsume organ system resources, and the animal may not be able tomaintain homeostasis. The compromised state of an animal in thiscondition causes a reduction in the efficiencies of the animal'smetabolic system, which in turn manifest in consequences such as adecreased daily weight gain or observable increases in morbidity.

Therefore, in the case of the bovine, in addition to an acute lung scorefor lung pathology detection, there is a need to determine whichproduction animals may be prone to not tolerating metabolic stress ormay be inefficient in adapting to metabolic stresses that could resultin poor performance over time. The poor performance can range frominadequate weight gain to acute morbidity and/or mortality.

While there may be some known methods and systems that account forcardiac performance in determining the health status of animals, thereis a further need to provide new ways of determining when animals thatare not capable of tolerating metabolic stress so that very earlypredictions can be made about the performance potential of an animals.

SUMMARY OF THE INVENTION

According to the invention, vital signs of an animal are recorded in astress inducing situation or environment that would likely elicit anadrenergic sympathetic “flight or fight” reaction. This environmentmaximizes the likelihood of anomaly detect. The vital signs recordedinclude the heart and respiration rates. The vital signs are analyzed todetermine whether compensatory changes may reflect on the animal'sability to tolerate stresses that some conditions may place on thehealth of the animal. From the compensatory changes observed, variousconclusions can be made regarding better treatments that can be pursuedfor sick animals and for more accurately predicting outcomes for thoseanimals in terms of whether an animal can reach production standards, orwhether the animal may require excessive treatment and costs to reachproduction standards.

One example of an environment or situation that may elicit adrenergicsympathetic reactions is actions that take place during processing beefcattle at a feed yard upon arrival to the feed yard. In this situation,the animals are typically agitated and therefore show cardio-pulmonarysigns of sympathetic adrenergic stress reactions including tachycardia(rapid heart rate) and tachypnea (rapid breathing). In another example,the invention contemplates use of a controlled stress producing eventwhich is known to generate adrenergic responses in animals. The purposeof this controlled stress producing event is to normalize the effects ona group of animals so there is less of a likelihood of random stressresponses with the events that could disproportionately influence aselected few animals.

The invention as described in more detailed below is a result of furtherobservations and conclusions regarding cardiac and pulmonary(cardio-pulmonary) subsystems of an animal. These cardio-pulmonarysubsystems of an animal can conceptually be described as subsystems thatwork together to create a synergistic outcome that maintains homeostasiswithin a given animal. In normal physiology, these subsystems respond tothe needs of the animal maintaining a constant relative relationship ofefficiencies and performance. That is, in times of high metabolicdemands both cardiac and pulmonary systems will increase functionaloutput to accommodate the change in metabolism. This is best observedduring physical exercise. If metabolic demands drop to a basal level,then correspondingly so will the level of performance for each componentof the cardio-pulmonary system. These high and basal metabolicfluctuations in rates for a normal functioning cardio-pulmonary systemscan describe a relatively constant ratio between cardiac output andpulmonary performance. However, if either the cardiac output orpulmonary performance is inefficient, then this relative ratio will bealtered especially as the metabolic demands increase or in times ofstress both physical and psychological. So it is with this ratio(Cardio-Pulmonary Ratio or CPR) that we can observe relative differencesin subsystem efficiencies. These differences can manifest in terms ofcompensatory changes to the heart rate and breath rate which allow forany deficits in efficiencies to be mitigated. Changes in either thecardiac or pulmonary system efficiencies can occur due to disease (acuteand chronic) or congenital defects. The concept of the cardio-pulmonaryratio (CPR) of the present invention is focused on the relativerelationship between the heart rate and breath rate and not theirabsolute values. For example; an animal with a high fever and normalcardio-pulmonary function will have elevations in both heart rate andbreath rate thus maintaining the relationship between the two systems inthe context of high absolute values on the rates of each subsystem. Ananimal with impaired organ function in the cardio-pulmonary system willlikely need to compensate for the impaired organ's inefficiencies andproduce a discordant rate drive in either the cardiac or pulmonarysystem. The compensation can result in a normal appearing animal butwill leave the animal less adaptable functional performance room in theface of stressors such as disease or weight gain or weather andhydration.

According to the invention, an assumption is made that there isgenerally a linear relationship between heart rate and respiratory rate.Thus the higher the heart rate, the proportionately higher therespiratory rate must be to match ventilation requirements. Anomaliescan be detected when the relationship deviates from an expected lineartrend. Thus higher heart rates with disproportionately lower respiratoryrates or lower heart rates with disproportionately higher respiratoryrates may indicate cardio-pulmonary performance abnormalities.

The linear trends can be expressed in terms of data points normalizedwith values plotted as curves on a graph. Cardio-pulmonary performanceabnormalities can therefore be shown as curve deviations that mayindicate heightened risks for diseases such as; BRD, acidosis, ketosis,or Brisket disease at lower elevations. These deviations can benumerically quantified and correlated to odds or chances that aparticular animal has or will develop the condition or disease. Thecardio-pulmonary status of an observed animal can be expressed as acardio-pulmonary rate ratio profile, and animals can then be sorted bytheir cardio-pulmonary rate ratio profiles to place the animals in anoptimized management program given their capacities or lack thereof totolerate stress.

Cardiopulmonary data can be obtained using an electronic stethoscope,such as disclosed in the U.S. application Ser. No. 13/442,569 entitledSystem and Method for Diagnosis of Bovine Diseases Using AuscultationAnalysis, this application being incorporated by reference herein in itsentirety. One minor change that could be incorporated within theelectronic stethoscope disclosed in that US application is thatnormally, the cardio generated sounds are filtered to therefore amplifyand clarify respiratory sounds. However in the present case, theelectronic stethoscope is used to obtain simultaneous data on both therespiration rate and heart rate of the observed animal; accordingly,cardio sounds do not require filtering.

The detected anomalies for deviations in the linear relationship betweenrespiration rate and heart rate can be mathematically expressed by firstapplying a formula to measured heart rates and respiration rates toplace the corresponding rates on a normal or bell shaped curve createdby sampling a large set of lung sounds. For this invention, 70,000sounds were used to generate the averages and distributions. This givesrelevance to the rates now expressed with a value between 0 and 1representing their position on the bell shaped curve. Thecardio-pulmonary rate ratio is then determined by dividing the finalnormalized adjusted heart rate value by the final normalized adjustedrespiratory value. From the values calculated for each of the sampleanimals and their heart and respiratory rates, a usage range isestablished on both curves which indicates the lower and upper bounds ofvalues used in the calculated ratio. This calculated cardio pulmonaryrate ratio is then also normalized to a value of 0 to 1 on the bellshaped curve.

The cardio pulmonary rate ratios (CPR) may be expressed as numericalscores, and these scores may be divided into categories that generallycharacterize the compensatory responses of the animals evaluated. Thefirst category is respiratory compensating (CPR-R) which corresponds tothose animals that generally compensate or respond to induced stressesby changes in respiration rates. The second category is cardiaccompensating (CPR-C) which corresponds to the animals that the generallycompensate or respond to induced stresses by changes in heart rates. Athird category is normal or non-compensating (CPR-N), which correspondsto those animals that do not exhibit suspect or out of range responsesto induced stresses. A fourth category combines animals from both therespiratory compensating and cardiac compensating groups into one groupof compensators as some disease etiologies can induce either a cardiacor respiratory compensatory response (CPR-RC). Accordingly, this grouprepresents those animals that have either a respiratory or a cardiaccompensating response.

CPR ratio values found at the lower end of the ratio range can be thoseratios having a value of 0.15 or less. These ratios indicate higherrelative respiration rates therefore indicating respiratory compensationdue to disease or other physiological anomalies. Accordingly, this rangeof values can be categorized as respiratory compensating (CPR-R).

At the highest end of the calculated CPR curve (0.85 or greater), thisrange of values corresponds to those animals that may have adisproportionately higher heart rate given their respiratory rate. It ispossible that these animals are compensating for a low cardiac output(CO) which could impact their ability to tolerate rapid weight gain.Accordingly, this range of values can be categorized as cardiaccompensating (CPR-C). Either end of the spectrum of ratios may indicatecompromised cardio-pulmonary function and therefore compensating animalsthat fall within the designated compensating categories are consideredsuspicious for normal cardio-pulmonary function.

CPR ratio values found between 0.15 and 0.85 can be categorized asnormal or non-compensating (CPR-N). Unless other observations are madewith animals having CPR ratio values within this range, there is ageneral presumption that these animals are not symptomatic for anyparticular ailment or anomaly.

Early detection of cardio-pulmonary compensating animals may enhanceoverall production and reduce production costs by sorting those animalswith significant inefficiencies into better suited production managementprograms. Using a CPR analysis for companion animals may assist indefining appropriate activities or direct treatments that improve thequality of life for the animal.

The present invention in broad terms provides CPR values for individualanimals in which a particular animal's CPR value or score can be placedwithin a normalized curve of data points for a population of animalswithin that specie and wherein each animal within the population ofanimals have respective CPR scores that were measured within the samestressed environment. Since the population has measured data pointswithin the same environmental conditions as a particular animal beingevaluated, this increases the likelihood that the conclusions made aboutthe particular animal are accurate predictions regarding the futurehealth of the animal, and its ability to reach production goals, or tootherwise perform according to expected standards.

One other aspect of the invention is that while respiratory rates doexpectedly correlate positively and significantly with animal bodytemperature, computed CPR values or scores are “body temperatureneutral” meaning there is no required measured parameter that correlatesCPR values with body temperature. Accordingly, the CPR values canprovide new information about the health condition of an animal withouthaving to obtain an animal's temperature. Further, the CPR valueprovides new information about the health condition of an animal withouthaving to obtain separate or additional data on auscultation, whetherthe auscultation is expressed in terms of a lung score or some othercalculated value.

According to another aspect of the invention, the use of the CPR valuescan be used in conjunction with auscultation data in order to identifynon-BRD pathology by fining animals without presumptive BRD fromauscultation data, but who are categorized as respiratory compensating(CPR-R). As further discussed in the detailed description, the non-BRDpathology analysis is yet another feature of the invention that can bederived from CPR values. From this analysis, predictive information canbe obtained regarding morbidity and mortality outcomes. One type ofauscultation analysis that is particularly useful with CPR values of thepresent invention is a lung scoring method disclosed in the abovementioned U.S. application Ser. No. 13/442,569 hereby incorporated byreference in its entirety. According to the invention disclosed in thisUS application, a system and method are described for diagnosis ofanimal respiratory diseases using auscultation techniques. Animal lungsounds are recorded and digitized. Lung sounds are obtained by anelectronic digital stethoscope or a wireless audio digital recordingunit. The sounds are stored as digital data, and one or more algorithmsare applied to the data for producing an output to the user indicativeof the health of the animal. Acoustic characteristics of the sound arecompared with baseline data in the algorithms. One embodiment includes adigital stethoscope with an integral display. Another embodimentprovides a system for gathering information about an animal to includenot only auscultation data, but also information from other fielddevices such as temperature probes or weigh scales. The combinedinformation can be analyzed by system software to generate detailedinformation to a user to include a diagnosis and recommended treatmentoptions. According to a method disclosed in this U.S. application Ser.No. 13/442,569, it includes a method for diagnosing animal diseasesusing auscultation analysis, said method comprising: (i) recordingauscultated sounds from an animal by an electronic digital stethoscopeand converting the sounds to digital data; (ii) converting the digitaldata to data in a frequency domain; (iii) separating data in thefrequency domain into predetermined desired groups of amplitudes andfrequencies forming converted data; (iv) applying an algorithm to theconverted data to generate at least one of a value or visual indicationthat corresponds to a state of health of the animal; (iv) providing anintegral display on the digital stethoscope; and (v) generating anoutput on the display for observation by a user indicating to the user astatus of health of the animal.

According to another method disclosed in this U.S. application Ser. No.13/442,569, it includes a system for gathering information regarding ananimal and using the information for determining a state of health ofthe animal, said system comprising: (i) a wireless electronic digitalstethoscope for recording auscultated lung sounds obtained from theanimal in the form of digital sound data; (ii) a processor forprocessing the digital sound data; (iii) computer coded instructions formanipulating the digital sound data through incorporation of at leastone algorithm used to calculate a value, said algorithm utilizingselected frequencies of the auscultated sounds, said algorithmgenerating a first set of data; (iii) said first set of data recorded ina database of said processor and said first set of data reflective of adiagnosis that corresponds to the values obtained from the algorithm;(iv) a user display incorporated on the digital stethoscope fordisplaying information reflective of a state of health of the animalcorresponding to the diagnosis and to additional health information; (v)at least one field device wirelessly communicating with the stethoscope,said field device including at least one of a weigh scale, an RFIDreader, a diagnostic device, and a temperature probe; and (vi) a secondset of data obtained from the field device as prompted by a pollingcommand from the stethoscope, wherein the second set of data correspondsto additional data obtained from the field device for the animal, andthe first and second data sets collectively are provided to the userdisplay corresponding to the additional health information. With respectto use of the stethoscope, the device may include a health statusindicator in the form of a plurality of health indicator lights. Theseindicator lights may represent a lung score, or may represent some otherindication as to the health of the animal. In one embodiment of thestethoscope, they may be numbered, for example, from 1-5. Theillumination of one of the lights or a group of lights indicates a lungscore or some other health status for the animal. For example, lightnumber one (1), if illuminated, could indicate a normal condition forthe animal. Light number two (2), if illuminated, could indicate a mild,acute condition. Light number three (3), if illuminated, could indicatea moderate acute condition. Light number four (4), if illuminated, couldindicate a severe acute condition, and light number five (5), ifilluminated, could indicate a chronic condition. As further discussed inthe detailed description, the non-BRD pathology analysis is yet anotherfeature of the invention that can be derived from CPR values inconjunction with the auscultation data.

According to yet another feature of the invention, use of the CPR valueswith auscultation data or information can be provided to generateimproved risk stratification of BRD cases with categorized lung scoregroups from the auscultation data. For example, use of the CPR valuescan produce predictive information for morbidity and mortality outcomesbased on lung score groups or categories.

According to yet another aspect of the invention, the use of CPR valuescan be used to detect abnormalities in the lungs of an animal, such aslung lesions that impact cardiovascular performance and may thereforeindicate a long-term decrease in production performance of the animal.One particularly advantageous aspect of the invention is that a simpleanalysis of only respiratory rate and heart rate will not allow areasonable conclusion to be made regarding lung abnormalities such aslung lesions, since there is no identifiable pattern of associationbetween absolute heart and breath rates. Through CPR, which transformsthe absolute rate values into clinically meaningful information, anassociation between the animal's vital sign rates and lung lesions isapparent and predictable.

According to yet another aspect of the invention, the use of CPR valuescan be used to predict performance metrics or performance measurements,such as the average daily weight gain of an animal in a feedlot. CPRvalues may be used in conjunction with other biometrics to drivereal-time best choice antibiotic and dietary treatment programs at pointof care.

According to yet another aspect of the invention, it can be used for aclinical decision making algorithm to drive treatment options based onpredicted outcomes of on-going analysis using digitized bio-metric datalike CPR values of the present invention. CPR values may be used asvital indicators of the overall health status of an animal and cancontribute to diagnostic information by transferring real-time data to arepository cloud database for modeling treatment efficacies. From themodeling, probable outcomes for animal performance can be createdincluding economic impact assessments that direct optimal diseasemanagement strategies such as best choice of antibiotic and dietarychanges. CPR values combined with a lung scoring algorithm and otheranimal health data such as health history, body temperature, previousdrug treatments, weight changes, weather reports, and morbidity andmortality rates can allow the creation of combinatorial optimizationtreatment suggestions to animal caregivers. A system of health datacapture according to this aspect of the invention can drive analyticalmodels that deliver evidence based medicine and adjust treatmentrecommendations from real-time feedback-loop information. Capturingphysiological information on animals and sending it immediately to acloud server for insertion into a machine learning module thatcommunicates immediately back to the point of care decision fortreatment may favorably shift morbidity and mortality trajectories, andat the same time minimize costs. This method of real-time feedbackregarding preferred treatments for individual animals may beparticularly advantageous with respect to use of antibiotics selectedfor treatment. Further, this method may enhance clinical decision makingof veterinarians on a day-to-day basis as they will have informationimmediacy and clinical relevance not available from prior systems ormethods. The use of CPR values in this method is ideal because CPRvalues incorporate diagnostic information that is applicable to thehealth of the whole animal and across multiple disease spectrums.

Considering the above features and attributes of the invention, theinvention can be further defined as a method for assessing animalsconsidering physiological responses to stress, comprising: (a) exposingan animal to a controlled environment known to induce sympatheticadrenergic stress reactions; (b) recording heart and respiration ratesof an animal during said reactions; (c) determining a cardiopulmonaryrate ratio for the animal expressed as the heart rate divided by therespiration rate; (d) determining a range of ratios for a plurality ofanimals within an observed population of animals; (e) determining agroup of first values for ratios indicating respiratory compensatingresponses (CPR-R); (f) determining a group of second values for ratiosindicating cardiac compensating responses (CPR-C); (g) determining agroup of third values for ratios indicating normal compensatingresponses (CPR-N); (h) determining a likelihood an animal will develop adisease taking into account said ratios within said first, second, orthird groups of values; and (i) providing treatment to the animalcorresponding to the likelihood the animal will develop the disease.Other aspects of the invention can be defined according to this methodfor assessing animals to further include any one of or any combinationof: (a) determining a weight for the animal and then determining alikelihood an animal will develop a disease taking into the weight ofthe animal (b) wherein the cardiopulmonary rate ratio is determined bydividing a final normalized adjusted heart rate value by a finalnormalized adjusted respiratory value (c) the cardiopulmonary rate ratiois determined by dividing a final normalized adjusted heart rate valueby a final normalized adjusted respiratory value (d) conducting anauscultation analysis for each animal and providing treatment to theanimal further considering results of said auscultation analysis (e)wherein the auscultation analysis further includes designation of a lungscore for the results corresponding to the analysis and (f) saidtreatment includes at least one of administration of an antibiotic,administration of a selected nutrition program, or combinations thereof.

Further considering the above features and attributes of the invention,the invention can be further defined as a method of establishing a CPRvalue for at least one animal within a population of similarly situatedanimals in a selected environment considering physiological responses tostress therein and using the CPR value for treatment, said methodcomprising:

(a) convert empirical distributions of breath and heart rates of ananimal into a standard normal distribution curve by: (i) recordingbreath and heart rates of a large sample of similar animals; similar inbreed, weight and health status: (ii) for breath rates, transform theempirical distribution into a standard normal distribution for use todetermine an animal's breath rate location on a cumulative normaldensity curve giving a value between 0 and 1; (iii) for heart rates;transform the empirical distribution into a standard normal distributionfor use to determine an animal's heart rate location on a cumulativenormal density curve giving a value between 0 and 1;

(b) develop CPR norms by: (i) calculating a raw CPR value from a valueof a corresponding normalized heart rate divided by a value of thebreath rate and applied only to animals with values greater than 0 onboth normalized breath and normalized heart rates; and (ii) taking theraw CPR values calculated and transform the empirical distribution ofthe raw CPR values into a standard normal distribution for use todetermine an animal's CPR value as a location on a cumulative normaldensity curve giving a value between 0 and 1;

(c) generate CPR norms by: (i) capture an animal's breath and heartrate; (ii) calculate a normalized breath rate cumulative density value(0 to 1) using the transformation determined; (iii) calculate anormalized heart rate cumulative density value (0 to 1) using thetransformation determined; (iv) calculate a ratio of the heart ratenormalized value to a breath rate normalized value; (v) calculate anormalized CPR value cumulative density value (0 to 1) using thetransformation equation determined; and (vi) assign a CPR category froma value using category determiners as follows:

-   -   i. If equal to or less than 0.15, then animal is categorized as        a respiratory compensator (CPR-R);    -   ii. If equal to or greater than 0.85, then animal is categorized        as a cardiac compensator (CPR-C); and    -   iii. If greater than 0.15 and less than 0.85, then animal is        categorized as a non-compensator/normal (CPR-N).

(d) reviewing determined CPR categories for animals selected fortreatment; and

(e) conducting treatment for the selected animals.

Yet further considering the above features and attributes of theinvention, the invention can be further defined as a method forassessing animals considering physiological responses to stress,comprising: exposing an animal to a controlled environment known toinduce sympathetic adrenergic stress reactions; recording heart andrespiration rates of an animal during said reactions; determining acardiopulmonary rate ratio for the animal expressed as the heart ratedivided by the respiration rate; determining a range of ratios for aplurality of animals within an observed population of animals; andproviding treatment to the animal corresponding to a likelihood theanimal will develop the disease by analyzing the cardiopulmonary rateratio. Other aspects of the invention can be defined according to thismethod for assessing animals to further include any one of or anycombination of: determining a group of first values for ratiosindicating respiratory compensating responses (CPR-R); determining agroup of second values for ratios indicating cardiac compensatingresponses (CPR-C); determining a group of third values for ratiosindicating normal compensating responses (CPR-N); and determining alikelihood an animal will develop a disease taking into account saidratios within said first, second, or third groups of values.

The above features of the inventions and others will become moreapparent from a review of the following detailed description, along withthe attached figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a visual depiction of example data points on a graph whichillustrate how CPR values of the present invention can be used withauscultation data to identify non-BRD pathology;

FIG. 2 is another depiction of example data points on a graph whichillustrate how CPR values of the present invention can be used withauscultation data to identify risk stratification of BRD as classifiedaccording to lung score groups;

FIG. 3 is a visual depiction of example data points showing lung lesionsidentified through ultransonography plotting by absolute values of heartand respiratory rates, and more particularly illustrating why lungailments such as lung lesions cannot be identified simply by recordingsuch data;

FIG. 4 is another visual depiction of example data points of lunglesions identified through ultransonography which illustrates plottingof heart and respiratory rates, but in which the rates for each axis arenormalized to a bell-shaped curve, in which normalization as a componentof CPR values of the present invention assist to better separate suspectanimals in terms of those having lung abnormalities such as lung lesionsand those without such abnormalities;

FIG. 5 is yet another visual depiction of example data points of lunglesions identified by ultrasonography which illustrates plotting ofheart and respiratory rates according to FIG. 4, with furtherinformation added to the graph including separation of the graph intozones corresponding to CPR-R, CPR-N, and CPR-C categories, and therelative abundance (more than twice the rate) of lung lesions in theCPR-R zone compared to the zones for CPR-N and CPR-C;

FIG. 6 is another visual depiction of example data points on a graphthat illustrates how knowing the percentage of CPR-C categorized animalsupon arrival to a location such as a feed yard can provide a moreaccurate prediction as to performance of animals group within a penwithin the feed yard.

DETAILED DESCRIPTION

The creation of CPR values for the present invention is optimized iflarge samples of data are used to establish norms that indicate truehigh and low values of vital signs within a species for a givenenvironment. CPR values can be determined for any species of animal inwhich preferably large samples of data are used to establish norms, andin which a preferred protocol for obtaining respiration and cardiacrates are to be taken from the same type of stress-induced environmentfor each animal. More specifically, the CPR values are more reliablewhen each animal of the population is exposed to the same or similarstress induced environment.

Set forth below is an example method/protocol of the invention forestablishing a CPR formula or mathematical expression for a species,such as a bovine species:

-   -   1. Convert empirical distribution of breath and heart rates of a        given species and breed into standard normal distribution        curves˜N(μ=0,σ=1).        -   a. Determine or capture the breath and heart rates of a            large sample of similar animals; similar in breed, weight            and health status. Preferably obtain captured data for many            animals.        -   b. For breath rates; using the data in step 1a, transform            the empirical distribution into a standard normal            distribution˜N(μ=0,σ=1) which can be used to determine an            animal's breath rate location on a cumulative normal density            curve giving a value between 0 and 1.        -   c. For heart rates; using the data in step 1a, transform the            empirical distribution into a standard normal            distribution˜N(μ=0,σ=1) which can be used to determine an            animal's heart rate location on a cumulative normal density            curve giving a value between 0 and 1.    -   2. Develop CPR norms.        -   a. For each animal in the sample; a raw CPR value is            calculated from the value of their normalized heart rate            (step 1c) divided by the value of the breath rate (step 1b).            This is applied only to those animals with values greater            than zero on both the normalized breath and normalized heart            rates.        -   b. Taking the ratio values created in step 2a, transform the            empirical distribution of the raw ratio values into a            standard normal distribution˜N(μ=0,σ=1) which can be used to            determine an animal's CPR score or value as a location on a            cumulative normal density curve giving a value between 0 and            1.    -   3. Utilization of CPR norms        -   a. Capture an animal's breath and heart rate.        -   b. Calculate the normalized breath rate cumulative density            value (0 to 1) using the transformation equation determined            in step 1b.        -   c. Calculate the normalized heart rate cumulative density            value (0 to 1) using the transformation equation determined            in step 1c.        -   d. Calculate the ratio of the heart rate normalized value            (step 3c) to the breath rate normalized value (step 3b).        -   e. Calculate the normalized CPR value cumulative density            value (0 to 1) using the transformation equation determined            in step 2b.        -   f. Assign the CPR category from value in step 3e using the            following determination cut-off points.            -   i. If equal to or less than 0.15, then animal is a                categorized as a respiratory compensator (CPR-R).            -   ii. If equal to or greater than 0.85, then animal is                categorized as a cardiac compensator (CPR-C).            -   iii. If greater than 0.15 and less than 0.85, then                animal is categorized as a non-compensator/normal                (CPR-N).

Based upon the foregoing explanation, one example formula to describe aCPR score or value may be expressed as follows:

CPR=ê(−(−10+(ASINH(((ê(−(0.6+(1.*LN(((LN(HeartRate)−4))/((6−LN(HeartRate))))))̂2/2)/ê(−(0.3+LN(((BreathRate−3))/((100−BreathRate))))̂2/2)+0.03))/0.00001)))̂2/2)√2π

Referring now to the Figures, FIG. 1 is a visual depiction of datapoints on a graph which illustrate how CPR values of the presentinvention can be used with auscultation data to identify non-BRDpathology. More specifically, FIG. 1 shows example data concerningclassification of CPR scores or values for a group of observed animals.The background information on the animals is that they arrived to alocation, such as a feedlot, and each animal in the group was previouslytreated with antibiotics. The number of animals in thepopulation/observed group is 1,069 animals. Each of the animals wereevaluated in terms of obtaining auscultation data, such as acorresponding lung score as disclosed in the above mentioned U.S.application Ser. No. 13/442,569. Each of the animals were also evaluatedby generating corresponding CPR scores, and specific data points shownin the graph correspond to groups of animals within the population thathad the corresponding CPR scores. As further shown in the graph, the twogeneral categories of CPR evaluations recorded include CPR-N and CPR-C.On the right side of the graph along a lung score of 4, as expected,there was a fairly high case fatality rate for those animals which had ahigh lung score and which were determined as having respiratorycompensating or cardiac compensating CPR scores. However, the graph alsoshows a high case fatality rate for one group of animals on the leftside of the graph along a lung score of 1. Although these animals hadpresumably healthy respiratory systems because of the low lung score,there was still a high fatality rate that cannot be explained by just anevaluation of auscultation data. This elevated case fatality rate isonly observable as a function of the determination of CPR scores forthis group of animals, and it can therefore be deduced that thatrelatively high fatality rate was due to non-BRD pathology. Theseanimals may also have been observed as not responding to antibiotic use;however, a determination of potential other diseases is simply notpossible with auscultation analysis. Therefore, one proposed or prudenttreatment that could take place for this group of animals is to withdrawthe animals from any antibiotics, and to memorize the animal from otherconditions which may contribute to something other than BRD, such as ametabolic disorder. In summary, FIG. 1 is therefore intended toillustrate that although an animal may have a favorable lung score,increased fatality rates for these types of animals can be difficult topredict unless there's some type of other measurement parameter whichmay provide a caregiver, a more thorough and comprehensive diagnosticanalysis of the state of health of the animal.

Referring to FIG. 2, another graph shows how CPR values of the presentinvention can be used with auscultation data to identify riskstratification of BRD as classified according to lung score groups. Asreflected in this figure, the data points correspond to a study ofgroups of animals characterized as either CPR-N or CPR-R, and thepopulation or sample was 15,937 head of cattle. There are fewconclusions that can be drawn from a review of these recorded datapoints. First, the graph shows that there was an increased fatality ratefor animals across all ranges of the lung scores when comparing animalsclassified as CPR-R versus CPR-N. In other words, the fatality rateincreased for animals having a respiratory compensating response asopposed to those animals that did not have a respiratory compensatingresponse, and this increase occurred even with animals having low lungscores, that is, those animals in which presumptive diagnoses could bemade regarding BRD by review of only auscultation data. As shown in thegraph, for observed animals having a lung score of 1, there was a 64.8%increase in mortality rates when comparing CPR-R versus CPR-N; forobserved animals having a lung score of 2, there was a 37.6% increase inmortality rates when comparing CPR-R versus CPR-N; for observed animalshaving a lung score of 3, there was a 11.5% increase in mortality rateswhen comparing CPR-R versus CPR-N; for observed animals having a lungscore of 4, there was a 55.5% increase in mortality rates when comparingCPR-R versus CPR-N; and for observed animals having a lung score of 5,there was a 75.4% increase in mortality rates when comparing CPR-Rversus CPR-N. Another general conclusion that can be drawn from the datashown in this graph is that some animals classified in one lung scorewith CPR-R should be considered for a different treatment protocolbecause the increased mortality rate places or qualifies him forconsideration for treatment in a different lung score/category. Morespecifically, the animals categorized as CPR-R with a lung score of 2had a slightly higher mortality rate than those animals classified asCPR-N and a lung score of 3. Therefore, a caregiver may wish to alterthe treatment protocol for these animals to correspond to the treatmentbeing given for animals having a lung score of 3.

Referring to FIG. 3, this graph provides a visual depiction of datapoints which illustrates plotting of heart and respiratory rates, andmore particularly illustrates why lung ailments such as lung lesionscannot be identified simply by evaluating heart and respiratory rates.More specifically, FIG. 3 illustrates heart and respiratory rate datafor a group of animals that were studied to detect the presence of lunglesions. The presence of lung lesions negatively impacts cardiovascularperformance and typically corresponds to long-term decreases inproduction performance. The study included 210 cow calves under 150pounds, and the animals were analyzed to obtain both lung scores and CPRscores. The presence of lung lesions in the animals were verified byconducting ultrasounds giving CPR a diagnostic sensitivity for lunglesions of 0.82 at a peripheral lung depth of 2 cm or more. The animalswith lung lesions as compared to those without lung lesions wereindistinguishable in terms of identifiable differences in heart orrespiration rates. In other words, by review of only heart andrespiratory rates, no conclusions could be made as to differencesbetween the animals. Therefore, it is apparent that a traditionalauscultation analysis could not assist in easily distinguishing animalsfor purposes of detecting lung lesions.

Referring to FIG. 4, another visual depiction of data points is shown ona graph illustrating plotting of heart and respiratory rates; however,respiratory rates are normalized to a bell-shaped curve, andnormalization of the respiratory rates provides an improved indicationas to how to distinguish between animals that may have lung lesions. Insummary, FIG. 4 illustrates that normalizing the data for the breathrates produces a bell-shaped curve which can be used as more usefulinformation regarding the impact of lung lesions because it can be seenthat the lung lesions are much more prevalent in the top 50% of the bellcurve as compared to the bottom 50% area. By normalizing the absoluterates, meaningful relationships can be defined between breath ratevalues beyond their absolute differences. That is; a breath ratedifference between 65/min and 55/min is 10/min but the same differencebetween, for example, 95/min and 85/min is a much rarer occurrence as95/min is at the tail end of the upper distribution and can beconsidered almost a statistical outlier. In summary, respiratory ratesabove a value of 0.50 show much greater density in terms of the numberof animals who were detected having lung lesions according to theresults of the verifying ultrasound procedures. Normalization is acomponent of determining CPR values of the present invention andtherefore, normalization in this figure indicates that by placingrelative frequencies of breath rate occurrences, interpretation of theinformation is fundamentally changed by adding a new dimension to thedata.

Referring to FIG. 5, this is yet another visual depiction of data pointson a graph which illustrates plotting of heart and respiratory ratesaccording to FIG. 4, with further information added to the graphincluding separation of the graph into zones corresponding to CPR-R,CPR-N, and CPR-C categories. More specifically, FIG. 5 shows adistribution of minimum lesion depths (2.0 cm) as measured by theconfirmatory ultrasounds in which a separation of the graph into thezones provides valuable information regarding those animals that shouldbe targeted for treatment. The dotted line extending from the origin inan upwards manner to approximately 0.20 on the horizontal axis separatesdata points for those animals classified as CPR-R and CPR-N. The datapoints for the group of animals to the left of this line are classifiedas CPR-R, while the data points for the group of animals to the right ofthis line are classified as CPR-N. The dotted line extending from theorigin in a more flat manner and terminating near 1.0 on the horizontalaxis separates data points for the animals classified as CPR-N andCPR-C. The data points for the group of animals above and to the left ofthis line are the animals classified as CPR-N, while the data points forthe group of animals below and to the right of this line are the animalsclassified as CPR-C. One general conclusion that can be made from theuse of CPR data in this graph is a prediction of lung lesions known tobe associated with lower performance and higher morbidity, and thisgroup of animals correspond to those classified in CPR-R. As shown, theanimals classified in this group have more than twice the rate of lunglesions than the other two classified groups of animals. Accordingly,these animals could be selectively treated on arrival to minimizeinfection spread and minimize re-infection rates. Use of this treatmentapproach supports best treatment practices to include good antibioticstewardship and judicious use only for those animals with a diagnosis.Other treatment approaches may be adopted considering other diseasesthat can be diagnosed early by classification of animals fromcorresponding CPR categories.

FIG. 6 is another graph that illustrates how knowing the percentage ofCPR-C categorized animals upon arrival to a location such as a feed yardcan provide a more accurate prediction as to performance of animalsgroup within a pen within the feed yard. More specifically, FIG. 6illustrates that CPR is capable of predicting closeout average dailygain (ADG) by evaluating, for example, a pen lot considering animalscharacterized as CPR-C. This figure shows to groups of animals, namely,steers and mixed. The vertical axis shows average daily gain atcloseout, and the effect of more pronounced cardiac compensatingresponse animals which have a lower average daily gain as compared tothose animals which have less pronounced cardiac compensating responses.This figure also shows the difference between steers and mixed, and theoverall increased ability for steers to gain weight as compared to mixedanimals across a large range of CPR-C values. From an economicstandpoint, the linear relationships that can be seen in the graph forboth steers and mixed in terms of average daily gain, one may moreaccurately predict when groups of animals may actually attain desiredweight gain goals. Therefore, a more accurate prediction in terms ofcloseout dates can provide numerous advantages.

It should be understood that the method of the invention can be executedwithin a data processing system in which the mathematical calculationsconducted for the CPA scores and other mathematical calculationsrelating to auscultation data are manipulated, stored, and madeavailable to a user in various user interface displays. For storage andcalculation of data, this can be achieved on a data processing networkor within respective standalone data computer systems, depending uponhow a user may wish to use and secure the data. It is furthercontemplated that functionality associated with displaying the resultsof CPA scores and corresponding auscultation data can be presented to auser on conventional user interface displays, such as screen displays onpersonal computers, screen displays on mobile devices, and others. FIGS.1-6 represent exemplary graphs that may be used as displays for data toa user to evaluate and compare groups of animals according to variousobserved characteristics, to include not only CPR and auscultation data,but any other measured parameters such as animal weight, days on feed,days on antibiotic, and others.

What is claimed is:
 1. A method for assessing animals consideringphysiological responses to stress, comprising: exposing an animal to acontrolled environment known to induce sympathetic adrenergic stressreactions; recording heart and respiration rates of an animal duringsaid reactions; determining a cardiopulmonary rate ratio for the animalexpressed as the heart rate divided by the respiration rate; determininga range of ratios for a plurality of animals within an observedpopulation of animals; determining a group of first values for ratiosindicating respiratory compensating responses (CPR-R); determining agroup of second values for ratios indicating cardiac compensatingresponses (CPR-C); determining a group of third values for ratiosindicating normal compensating responses (CPR-N); determining alikelihood an animal will develop a disease taking into account saidratios within said first, second, or third groups of values; andproviding treatment to the animal corresponding to the likelihood theanimal will develop the disease.
 2. A method, as claimed in claim 1,further including: determining a weight for the animal; and determininga likelihood an animal will develop a disease taking into the weight ofthe animal.
 3. A method, according to claim 1, wherein: saidcardiopulmonary rate ratio is determined by dividing a final normalizedadjusted heart rate value by a final normalized adjusted respiratoryvalue.
 4. A method, according to claim 1, further including: conductingan auscultation analysis for each animal; and providing treatment to theanimal further considering results of said auscultation analysis.
 5. Amethod, according to claim 4, wherein: said auscultation analysisfurther includes designation of a lung score for the resultscorresponding to the analysis.
 6. A method, according to claim 1,wherein: said treatment includes at least one of administration of anantibiotic, administration of a selected nutrition program, orcombinations thereof.
 7. A method of establishing a CPR value for atleast one animal within a population of similarly situated animals in aselected environment considering physiological responses to stresstherein and using the CPR value for treatment, said method comprising:(a) convert empirical distributions of breath and heart rates of ananimal into a standard normal distribution curve by: (i) recordingbreath and heart rates of a large sample of similar animals; similar inbreed, weight and health status: (ii) for breath rates, transform theempirical distribution into a standard normal distribution for use todetermine an animal's breath rate location on a cumulative normaldensity curve giving a value between 0 and 1; (iii) for heart rates;transform the empirical distribution into a standard normal distributionfor use to determine an animal's heart rate location on a cumulativenormal density curve giving a value between 0 and 1; (b) develop CPRnorms by: (i) calculating a raw CPR value from a value of acorresponding normalized heart rate divided by a value of the breathrate and applied only to animals with values greater than 0 on bothnormalized breath and normalized heart rates; and (ii) taking the rawCPR values calculated and transform the empirical distribution of theraw CPR values into a standard normal distribution for use to determinean animal's CPR value as a location on a cumulative normal density curvegiving a value between 0 and 1; (c) generate CPR norms by: (i) capturean animal's breath and heart rate; (ii) calculate a normalized breathrate cumulative density value (0 to 1) using the transformationdetermined; (iii) calculate a normalized heart rate cumulative densityvalue (0 to 1) using the transformation determined; (iv) calculate aratio of the heart rate normalized value to a breath rate normalizedvalue; (v) calculate a normalized CPR value cumulative density value (0to 1) using the transformation equation determined; and (vi) assign aCPR category from a value using category determiners as follows: Ifequal to or less than 0.15, then animal is categorized as a respiratorycompensator (CPR-R); If equal to or greater than 0.85, then animal iscategorized as a cardiac compensator (CPR-C); and If greater than 0.15and less than 0.85, then animal is categorized as anon-compensator/normal (CPR-N). (d) reviewing determined CPR categoriesfor animals selected for treatment; and (e) conducting treatment for theselected animals.
 8. A method, according to claim 7, further including:conducting an auscultation analysis for each animal; and providingtreatment to the animal further considering results of said auscultationanalysis.
 9. A method, according to claim 8, wherein: said auscultationanalysis further includes designation of a lung score for the resultscorresponding to the analysis.
 10. A method, according to claim 7,wherein: said treatment includes at least one of administration of anantibiotic, administration of a selected nutrition program, orcombinations thereof.
 11. A method for assessing animals consideringphysiological responses to stress, comprising: exposing an animal to acontrolled environment known to induce sympathetic adrenergic stressreactions; recording heart and respiration rates of an animal duringsaid reactions; determining a cardiopulmonary rate ratio for the animalexpressed as the heart rate divided by the respiration rate; determininga range of ratios for a plurality of animals within an observedpopulation of animals; and providing treatment to the animalcorresponding to a likelihood the animal will develop the disease byanalyzing the cardiopulmonary rate ratio.
 12. A method, according toclaim 11, further including: determining a group of first values forratios indicating respiratory compensating responses (CPR-R);determining a group of second values for ratios indicating cardiaccompensating responses (CPR-C); determining a group of third values forratios indicating normal compensating responses (CPR-N); and determininga likelihood an animal will develop a disease taking into account saidratios within said first, second, or third groups of values.
 13. Amethod, according to claim 11, further including: conducting anauscultation analysis for each animal; and providing treatment to theanimal further considering results of said auscultation analysis.
 14. Amethod, according to claim 13, wherein: said auscultation analysisfurther includes designation of a lung score for the resultscorresponding to the analysis.
 15. A method, according to claim 11,wherein: said treatment includes at least one of administration of anantibiotic, administration of a selected nutrition program, orcombinations thereof.